Personal assistant for facilitating interaction routines

ABSTRACT

In one example, the present disclosure describes a device, computer-readable medium, and method for automatically learning and facilitating interaction routines involving at least one human participant. In one example, a method includes learning an interaction routine conducted between a human user and a second party, wherein the interaction routine comprises a series of prompts and responses designed to identify and deliver desired information, storing a template of the interaction routine based on the learning, wherein the template includes at least a portion of the series of prompts and responses, detecting, in the course of a new instance of the interaction routine, at least one prompt from the second party that requests a response from the human user, and using the template to provide a response to the prompt so that involvement of the human user in the new instance of the interaction routine is minimized.

The present disclosure relates generally to machine learning, andrelates more particularly to devices, non-transitory computer-readablemedia, and methods for automatically learning and facilitatinginteraction routines involving at least one human participant.

BACKGROUND

Certain interactions are characterized by fairly predictable, expectedroutines. For instance, performing a banking transaction, checking thestatus of an airline flight, and making a retail purchase all involveexchanges of certain expected types of information. As an example, if aperson wishes to check on the status of an airline flight, he or she maybe asked to supply the name of the airline, the flight number, theorigin or destination airport, or other information that would help acustomer service representative or an automated system identify theairline flight for which the status is sought. The customer servicerepresentative or automated system may respond with a status identifierfor the airline flight in question, such as “On time” or “Delayed.”

SUMMARY

In one example, the present disclosure describes a device,computer-readable medium, and method for automatically learning andfacilitating interaction routines involving at least one humanparticipant. In one example, a method includes learning an interactionroutine conducted between a human user and a second party, wherein theinteraction routine comprises a series of prompts and responses designedto identify and deliver desired information, storing a template of theinteraction routine based on the learning, wherein the template includesat least a portion of the series of prompts and responses, detecting, inthe course of a new instance of the interaction routine, at least oneprompt from the second party that requests a response from the humanuser, and using the template to provide a response to the prompt so thatinvolvement of the human user in the new instance of the interactionroutine is minimized.

In another example, a device includes a processor and acomputer-readable medium storing instructions which, when executed bythe processor, cause the processor to perform operations. The operationsinclude learning an interaction routine conducted between a human userand a second party, wherein the interaction routine comprises a seriesof prompts and responses designed to identify and deliver desiredinformation, storing a template of the interaction routine based on thelearning, wherein the template includes at least a portion of the seriesof prompts and responses, detecting, in the course of a new instance ofthe interaction routine, at least one prompt from the second party thatrequests a response from the human user, and using the template toprovide a response to the prompt so that involvement of the human userin the new instance of the interaction routine is minimized.

In another example, a computer-readable medium storing instructionswhich, when executed by a processor, cause the processor to performoperations. The operations include learning an interaction routineconducted between a human user and a second party, wherein theinteraction routine comprises a series of prompts and responses designedto identify and deliver desired information, storing a template of theinteraction routine based on the learning, wherein the template includesat least a portion of the series of prompts and responses, detecting, inthe course of a new instance of the interaction routine, at least oneprompt from the second party that requests a response from the humanuser, and using the template to provide a response to the prompt so thatinvolvement of the human user in the new instance of the interactionroutine is minimized.

BRIEF DESCRIPTION OF THE DRAWINGS

The teachings of the present disclosure can be readily understood byconsidering the following detailed description in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates an example network related to the present disclosure;

FIG. 2 illustrates a flowchart of an example method for learning aninteraction routine involving at least one human participant;

FIG. 3 illustrates a flowchart of an example method for automaticallyconducting an interaction involving at least one human participant,using a learned interaction routine; and

FIG. 4 depicts a high-level block diagram of a computing devicespecifically programmed to perform the functions described herein.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe figures.

DETAILED DESCRIPTION

In one example, the present disclosure automatically learns andfacilitates interaction routines (i.e., repeated or predictable seriesof steps used to conduct interaction) involving at least one humanparticipant. As discussed above, certain interactions are characterizedby fairly predictable, expected routines (e.g., a series of prompts andresponses designed to identify and deliver desired information).However, the mechanism through which these routines are performed mayvary. For instance, if a person wishes to check on the status of anairline flight, he may call a customer service number that connects himto a human representative, an automated system (e.g., an interactivevoice response (IVR) system), or to both a human representative and anautomated system. Moreover, different airlines may implement differenttypes of systems for providing flight status information. For instance,a first airline may provide flight status information exclusively via anautomated system, while a second airline may provide flight statusinformation via a combination of an automated system and a humanrepresentative. Thus, although the act of calling a customer servicenumber to check on the status of a flight may be a fairly routineprocess, the specifics of the interaction may vary from case to case. Assuch, if a person is not familiar with a particular system, he maydevote a substantial amount of time and effort to completing even asimple transaction using the system.

Examples of the present disclosure employ a combination of machinelearning and natural language understanding techniques in order toobserve and detect patterns or routines in a human user's interactionswith an interactive system. In one example, the interactive system couldbe an automated system (e.g., an IVR system), another human (e.g., acustomer service representative), or a combination of an automatedsystem and another human (e.g., an automated system that collectscertain information from the user before directing him to a particularcustomer service representative). Once an interaction routine isdetected, the proper user responses can be learned, stored, and used tofacilitate future interactions with minimal user assistance.

Although examples of the disclosure are described within the context ofa telephone call, it will be appreciated that the examples could be usedto facilitate and conduct any type of interaction routine involving atleast one human user. For instance, the disclosure could just as easilybe used to facilitate and conduct an interaction routine over a texttelephone, a web-based access portal, or a web-based chat feature.

To better understand the present disclosure, FIG. 1 illustrates anexample network 100, related to the present disclosure. The network 100may be any type of communications network, such as for example, atraditional circuit switched network (CS) (e.g., a public switchedtelephone network (PSTN)) or an Internet Protocol (IP) network (e.g., anIP Multimedia Subsystem (IMS) network, an asynchronous transfer mode(ATM) network, a wireless network, a cellular network (e.g., 2G, 3G andthe like), a long term evolution (LTE) network, and the like) related tothe current disclosure. It should be noted that an IP network is broadlydefined as a network that uses Internet Protocol to exchange datapackets. Additional exemplary IP networks include Voice over IP (VoIP)networks, Service over IP (SoIP) networks, and the like.

In one example, the network 100 may comprise a core network 102. In oneexample, core network 102 may combine core network components of acellular network with components of a triple play service network; wheretriple play services include telephone services, Internet services, andtelevision services to subscribers. For example, core network 102 mayfunctionally comprise a fixed mobile convergence (FMC) network, e.g., anIP Multimedia Subsystem (IMS) network. In addition, core network 102 mayfunctionally comprise a telephony network, e.g., an InternetProtocol/Multi-Protocol Label Switching (IP/MPLS) backbone networkutilizing Session Initiation Protocol (SIP) for circuit-switched andVoice over Internet Protocol (VoIP) telephony services. Core network 102may also further comprise an Internet Service Provider (ISP) network. Inone embodiment, the core network 102 may include an application server(AS) 104 and a database (DB) 106. Although only a single AS 104 and asingle DB 106 are illustrated, it should be noted that any number ofapplication servers 104 or databases 106 may be deployed. Furthermore,for ease of illustration, various additional elements of core network102 are omitted from FIG. 1.

In one embodiment, the AS 104 may comprise a general purpose computer asillustrated in FIG. 4 and discussed below. In one embodiment, the AS 104may perform the methods discussed below related to learning andfacilitating interaction routines involving at least one humanparticipant. For instance, the AS 104 may monitor user interactions,detect routines or patterns in those interactions, and use thoseroutines or patterns to facilitate future interactions.

In one embodiment, the DB 106 may store data relating to detectedroutines or patterns in a user's interactions. For example, the DB 106may store user profiles, which users or the AS 104 can updatedynamically at any time. User profiles may include contact information(e.g., mobile phone number, email address, etc.) for the user and/or forcontacts (e.g., other people and/or automated systems) with whom theuser interacts or has interacted. User profiles may also include seriesof steps and/or desired responses for particular interaction routines.User profiles may be stored in encrypted form to protect user privacy.

The core network 102 may be in communication with one or more wirelessaccess networks 120 and 122. Either or both of the access networks 120and 122 may include a radio access network implementing suchtechnologies as: global system for mobile communication (GSM), e.g., abase station subsystem (BSS), or IS-95, a universal mobiletelecommunications system (UMTS) network employing wideband codedivision multiple access (WCDMA), or a CDMA3000 network, among others.In other words, either or both of the access networks 120 and 122 maycomprise an access network in accordance with any “second generation”(2G), “third generation” (3G), “fourth generation” (4G), Long TermEvolution (LTE), or any other yet to be developed futurewireless/cellular network technology including “fifth generation” (5G)and further generations. The operator of core network 102 may provide adata service to subscribers via access networks 120 and 122. In oneembodiment, the access networks 120 and 122 may all be different typesof access networks, may all be the same type of access network, or someaccess networks may be the same type of access network and other may bedifferent types of access networks. The core network 102 and the accessnetworks 120 and 122 may be operated by different service providers, thesame service provider or a combination thereof.

In one example, the access network 120 may be in communication with oneor more user endpoint devices (also referred to as “endpoint devices” or“UE”) 108 and 110, while the access network 122 may be in communicationwith one or more user endpoint devices 112 and 114. Access networks 120and 122 may transmit and receive communications between respective UEs108, 110, 112, and 114 and core network 102 relating to communicationswith web servers, AS 104, and/or other servers via the Internet and/orother networks, and so forth.

In one embodiment, the user endpoint devices 108, 110, 112, and 114 maybe any type of subscriber/customer endpoint device configured forwireless communication such as a laptop computer, a Wi-Fi device, aPersonal Digital Assistant (PDA), a mobile phone, a smart phone, anemail device, a computing tablet, a messaging device, a wearable “smart”device (e.g., a smart watch or fitness tracker), a portable media device(e.g., an MP3 player), a gaming console, a portable gaming device, andthe like. In one example, the user endpoint devices 108, 110, 112, and114 are capable of receiving audible (e.g., spoken) and/or manual (e.g.,button press, text) inputs from a user and of providing audible (e.g.,spoken, tone) and/or visible (e.g., text) outputs to the user. One ormore of the user endpoint devices may comprise an automated interactivesystem, such as an IVR system. In one example, any one or more of theuser endpoint devices 108, 110, 112, and 114 may have both cellular andnon-cellular access capabilities and may further have wiredcommunication and networking capabilities. It should be noted thatalthough only four user endpoint devices are illustrated in FIG. 1, anynumber of user endpoint devices may be deployed.

It should also be noted that as used herein, the terms “configure” and“reconfigure” may refer to programming or loading a computing devicewith computer-readable/computer-executable instructions, code, and/orprograms, e.g., in a memory, which when executed by a processor of thecomputing device, may cause the computing device to perform variousfunctions. Such terms may also encompass providing variables, datavalues, tables, objects, or other data structures or the like which maycause a computer device executing computer-readable instructions, code,and/or programs to function differently depending upon the values of thevariables or other data structures that are provided. For example, anyone or more of the user endpoint devices 108, 110, 112, and 114 may hostan operating system for presenting a user interface that may be used tosend data to another user endpoint device 108, 110, 112, or 114 and forreviewing data sent by the other user endpoint device.

Those skilled in the art will realize that the network 100 has beensimplified. For example, the network 100 may include other networkelements (not shown) such as border elements, routers, switches, policyservers, security devices, a content distribution network (CDN) and thelike. The network 100 may also be expanded by including additionalendpoint devices, access networks, network elements, applicationservers, etc. without altering the scope of the present disclosure.

FIG. 2 illustrates a flowchart of an example method 200 for learning aninteraction routine involving at least one human participant. In oneexample, the method 200 may be performed by an application server, e.g.,AS 104 of FIG. 1. In another example, the method 200 may be performed bya user endpoint device, e.g., one of the UEs 108, 10, 112, or 114 ofFIG. 1. However, any references in the discussion of the method 200 toelements of FIG. 1 are not intended to limit the means by which themethod 200 may be performed.

The method 200 begins in step 202. In step 204, a new interaction isdetected. The new interaction may be initiated by a user (i.e., a humanuser), and may involve the user opening a communication connection(e.g., a phone call, a web-based chat window, an email, or the like) toa second party using a user endpoint device, such as a smart phone. Thesecond party may be another human, an automated system (e.g., an IVRsystem), or a combination of the two. The information used to contactthe second party (e.g., telephone number) may be recorded or stored upondetection of the new interaction. The new interaction may be detectedautomatically (e.g., without explicit user prompting), or the user mayexplicitly indicate that the new interaction is about to take place andmay request that the new interaction be monitored.

In step 206, a prompt from the second party is detected and recorded.For instance, the prompt may request certain information from the user.The prompt may be an audible prompt or a visible prompt. As an example,the prompt may present a menu of options, and may ask the user to selectone of the options by either speaking the selected option aloud or bypressing a button corresponding to the selected option (e.g., “Press 1for Billing, or say ‘Billing’”). Alternatively, the prompt may be anopen-ended prompt (e.g., the number or format of possible responses isnot fixed), to which the user may respond by speaking a verbal responseor by pressing a series of buttons (e.g., “State the reason for yourcall”). Natural language understanding may be used to identify an intentof the prompt.

In step 208, the user's response to the prompt is detected and recorded.The response may be an audible response or a visible response. As anexample, the response may be a spoken selection of an option chosen froma menu or a button press corresponding to an option chosen from a menu.Alternatively, the response may be an open-ended response (e.g., thenumber or format of possible responses is not fixed) in which the userspeaks a verbal response or presses a series of buttons. Naturallanguage understanding may be used to identify an intent of the responseand a relationship between the response and the prompt to which itresponds.

In step 210, it is determined whether another prompt from the secondparty has been detected. For instance, in some cases, providing aresponse to a first prompt may cause a new prompt to be presented, as inthe case with a menu that drills down. As an example, selecting a menuoption for “Billing,” may cause a new menu to be presented in which morespecific options such as “Pay new bill,” “Dispute a bill,” “Changebilling address,” and/or other options are presented. Natural languageunderstanding may be used to determine whether a communication from thesecond party is a new prompt requiring a response, an item of requestedinformation (e.g., confirmation that a bill has been paid), or somethingelse.

If it is determined in step 210 that another prompt from the secondparty has been detected, then the method 200 returns to step 206 andproceeds as described above to record the new prompt and the user'sresponse.

Alternatively, if it is determined in step 210 that another prompt fromthe second party has not been detected, then the method 200 proceeds tostep 212. In step 212, it is determined whether a prompt for a voicepassphrase has been detected. A voice passphrase is a fixed word orphrase that the user speaks in order to authorize an action. Forinstance, the voice passphrase may comprise a spoken password orpersonal identification number, or the voice passphrase may comprise avoice identification prompt. In the latter case, the second partyrequesting the voice passphrase may store a recording of the userspeaking the passphrase, and may compare a new utterance of the spokenpassphrase (e.g., made in response to the prompt) to the recording. Ifthe audible similarity between the recording and the new utterance doesnot meet a certain threshold, the action may be cancelled. Not allinteraction routines will require a voice passphrase to be provided;thus, this is why the method 200 determines in step 212 whether a voicepassphrase has been requested. Natural language understanding may beused to determine that a voice passphrase has been requested.

If it is determined in step 212 that a prompt for a voice passphrase hasnot been detected, then the method 200 proceeds directly to step 220,described below (i.e., steps 214,218 are skipped). Alternatively, if itis determined in step 212 that a prompt for a voice passphrase has beendetected, then the method 200 proceeds to step 214. In step 214, theuser's response to the prompt for the voice passphrase is detected andrecorded.

In step 216, the user is queried to determine whether the voicepassphrase should be provided automatically in future interactions. Forinstance, the user response recorded in step 214 could be played back inresponse to future prompts for the voice passphrase. However, some usersmay prefer to provide their voice passphrases personally each time thevoice passphrases are requested, in order to avoid the possibility ofinadvertently authorizing an unwanted action.

In step 218, the user's response to the query (e.g., “provide voicepassphrase automatically” or “do not provide voice passphraseautomatically”) is received and stored.

In step 220, the information that the user is seeking (e.g.,confirmation that a bill has been paid, an amount of a balance, adiscussion with a human representative, or the like) is detected.Natural language understanding may be used to determine that acommunication from the second party is responsive to the reason for theuser's call.

In step 222, at least a portion of the interaction (i.e., the prompts,corresponding responses, voice passphrases, user responses or queries,and/or requested information) are saved (e.g., in a user profile orother data structure storing user preferences), so that they may be usedto facilitate future interactions, potentially with the same secondparty or with another party. Thus, the saved information may serve as atemplate for conducting a similar interaction in the future.

The method 200 then ends in step 224.

Thus, the method 200 may be used to learn an interaction routine thatmay be repeated in the future. That is, by observing at least a firstinstance of the interaction routine, examples of the present disclosuremay be able to automatically facilitate and conduct a second instance ofthe interaction routine with minimal intervention from the user. Assuch, the learned interaction routine may be used as a template forfacilitating and conducting a similar future interaction, so that theuser does not need to remember menus and is spared the frustration ofwaiting in a potentially long queue. In further examples of the method200, the user may be queried for additional information where theinformation recorded during the monitored interaction is insufficientand/or ambiguous.

FIG. 3 illustrates a flowchart of an example method 300 forautomatically conducting an interaction involving at least one humanparticipant, using a learned interaction routine. In one example, themethod 300 may be performed by an application server, e.g., AS 104 ofFIG. 1. In another example, the method 300 may be performed by a userendpoint device, e.g., one of the UEs 108, 10, 112, or 114 of FIG. 1.However, any references in the discussion of the method 300 to elementsof FIG. 1 are not intended to limit the means by which the method 300may be performed.

The method 300 begins in step 302. In step 304, an automatic assistancefeature is enabled on a user endpoint device. The automatic assistancefeature may be a feature that facilitates and conducts a knowninteraction routine with an interactive system. In one example, theautomatic assistance feature is enabled automatically, e.g., upondetection that contact information for a known interactive system (e.g.,a phone number, email address, or other contact information for anautomated system (e.g., an IVR system), another human, or a combinationof the two). For instance, the entry of a phone number that is known tobe a customer service number for a bank may be detected. Alternatively,an explicit command from the user may be detected, where the explicitcommand requests enablement of the automatic assistance feature (e.g., acommand to call the customer service number to check on a balance).

In step 306, an interaction routine template is retrieved, e.g., from aremote database or from local memory, in response to the enablement ofthe automatic assistance feature. For instance, the type of interactiondesired by the user may be determined based on the enablement of theautomatic assistance feature, and a template corresponding to theinteraction routine for the interaction may be retrieved.

In step 308, a connection (i.e., a connection that enables two-waycommunication, such as a phone call, a web-based chat window, an email,or the like) to second party is launched, in response to the enablementof the automatic assistance feature. For instance, a phone call may beplaced to the second party, an email may be sent to the second party, orthe like.

In step 310, a prompt from the second party is detected. For instance,the prompt may request certain information from the user. The prompt maybe an audible prompt or a visible prompt. As an example, the prompt maypresent a menu of options, and may ask the user to select one of theoptions by either speaking the selected option aloud or by pressing abutton corresponding to the selected option (e.g., “Press 1 for Billing,or say ‘Billing’”). Alternatively, the prompt may be an open-endedprompt (e.g., the number or format of possible responses is not fixed),to which the user may respond by speaking a verbal response or bypressing a series of buttons (e.g., “State the reason for your call”).Natural language understanding may be used to identify an intent of theprompt.

In step 312, it is determined whether the interaction routine templateprovides a response to the prompt. For instance, if the prompt detectedin step 310 included the statement “Press 1 for Billing,” theinteraction routine template may indicate that a response indicating thepressing of the “1” button on the user's phone or computing deviceshould be provided. However, in some cases, a discrepancy may beencountered for which the interaction routine template does notspecifically provide a response. For instance, the template might not beupdated to account for a recent change in the interaction routine (e.g.,removal of or change to a particular prompt).

If it is determined in step 312 that the interaction routine templateprovides a response to the prompt, then a response to the prompt isformulated and provided in step 314, using the interaction routinetemplate. The response may be an audible response (e.g., synthesized orrecorded speech or a touch tone) or a visible response (e.g., aweb-based chat message).

However, if it is determined in step 312 that the interaction routinetemplate does not provide a response to the prompt, then an alternatemeans of formulating a response to the prompt is invoked in step 316.The alternate means could be one or more of a plurality of means. Forinstance, in one example, the user may be alerted and asked to intervenepersonally in the interaction. In this case, the method 200 may beinvoked to observe the user and to learn the change to the interactionroutine, allowing the interaction routine template to be updated. Inanother example, a pre-defined default response may be selected (e.g.,request assistance from a human representative, hang up, etc.). Inanother example, an adaptive technique may be employed to formulate aresponse on-the-fly. The adaptive technique may be a rules-basedtechnique that uses natural language understanding. For instance, if amenu option has simply changed from “Press 1 to pay bill” to “Press 5 topay bill,” then the response can simply indicate the pressing of abutton for 5 rather than 1.

Once a response has been formulated in accordance with either step 314or step 316, the method 300 proceeds to step 318. In step 318, it isdetermined whether another prompt from the second party has beendetected. As discussed above, in some cases, providing a response to afirst prompt may cause a new prompt to be presented, as in the case witha menu that drills down. Natural language understanding may be used todetermine whether a communication from the second party is a new promptrequiring a response, an item of requested information (e.g.,confirmation that a bill has been paid), or something else.

If it is determined in step 318 that another prompt from the secondparty has been detected, then the method 300 returns to step 312 andproceeds as described above to formulate and provide a response to thenew prompt.

Alternatively, if it is determined in step 318 that another prompt fromthe second party has not been detected, then the method 300 proceeds tostep 320. In step 320, it is determined whether a prompt for a voicepassphrase has been detected. Natural language understanding may be usedto determine that a voice passphrase has been requested.

If it is determined in step 320 that a prompt for a voice passphrase hasbeen detected, then the method 300 proceeds to step 322. In step 322,either a recording of the user speaking the voice passphrase isprovided, or the user is prompted to personally provide the voicepassphrase, depending on the user preference (which may be indicated inthe interaction routine template).

Once the voice passphrase has been provided in step 322, or if it isdetermined that a prompt for a voice passphrase has not been detected,the method 300 proceeds to step 324.

In step 324, the information that the user is seeking (e.g.,confirmation that a bill has been paid, an amount of a balance, adiscussion with a human representative, or the like) is detected.Natural language understanding may be used to determine that acommunication from the second party is responsive to the reason for theuser's call.

The method ends in step 326.

Although not expressly specified above, one or more steps of the methods200 or 300 may include a storing, displaying and/or outputting step asrequired for a particular application. In other words, any data,records, fields, and/or intermediate results discussed in the method canbe stored, displayed and/or outputted to another device as required fora particular application. Furthermore, operations, steps, or blocks inFIG. 2 or 3 that recite a determining operation or involve a decision donot necessarily require that both branches of the determining operationbe practiced. In other words, one of the branches of the determiningoperation can be deemed as an optional step. Furthermore, operations,steps or blocks of the above described method(s) can be combined,separated, and/or performed in a different order from that describedabove, without departing from the examples of the present disclosure.

FIG. 4 depicts a high-level block diagram of a computing devicespecifically programmed to perform the functions described herein. Forexample, any one or more components or devices illustrated in FIG. 1 ordescribed in connection with the methods 200 and 300 may be implementedas the system 400. For instance, a user endpoint device or anapplication server (such as might be used to perform the methods 200 or300) could be implemented as illustrated in FIG. 4.

As depicted in FIG. 4, the system 400 comprises a hardware processorelement 402, a memory 404, a module 405 for learning and facilitatinginteraction routines, and various input/output (I/O) devices 406.

The hardware processor 402 may comprise, for example, a microprocessor,a central processing unit (CPU), or the like. The memory 404 maycomprise, for example, random access memory (RAM), read only memory(ROM), a disk drive, an optical drive, a magnetic drive, and/or aUniversal Serial Bus (USB) drive. The module 405 for learning andfacilitating interaction routines may include circuitry and/or logic forperforming special purpose functions relating to the monitoring,learning, and conducting of interactions. The input/output devices 406may include, for example, a camera, a video camera, storage devices(including but not limited to, a tape drive, a floppy drive, a hard diskdrive or a compact disk drive), a receiver, a transmitter, a speaker, amicrophone, a transducer, a display, a speech synthesizer, a hapticdevice, an output port, or a user input device (such as a keyboard, akeypad, a mouse, and the like).

Although only one processor element is shown, it should be noted thatthe general-purpose computer may employ a plurality of processorelements. Furthermore, although only one general-purpose computer isshown in the Figure, if the method(s) as discussed above is implementedin a distributed or parallel manner for a particular illustrativeexample, i.e., the steps of the above method(s) or the entire method(s)are implemented across multiple or parallel general-purpose computers,then the general-purpose computer of this Figure is intended torepresent each of those multiple general-purpose computers. Furthermore,one or more hardware processors can be utilized in supporting avirtualized or shared computing environment. The virtualized computingenvironment may support one or more virtual machines representingcomputers, servers, or other computing devices. In such virtualizedvirtual machines, hardware components such as hardware processors andcomputer-readable storage devices may be virtualized or logicallyrepresented. However, in other examples, the computing environment isnot virtualized.

It should be noted that the present disclosure can be implemented insoftware and/or in a combination of software and hardware, e.g., usingapplication specific integrated circuits (ASIC), a programmable logicarray (PLA), including a field-programmable gate array (FPGA), or astate machine deployed on a hardware device, a general purpose computeror any other hardware equivalents, e.g., computer readable instructionspertaining to the method(s) discussed above can be used to configure ahardware processor to perform the steps, functions and/or operations ofthe above disclosed method(s). In one example, instructions and data forthe present module or process 405 for learning and facilitatinginteraction routines (e.g., a software program comprisingcomputer-executable instructions) can be loaded into memory 404 andexecuted by hardware processor element 402 to implement the steps,functions or operations as discussed above in connection with theexample methods 200 or 300. Furthermore, when a hardware processorexecutes instructions to perform “operations,” this could include thehardware processor performing the operations directly and/orfacilitating, directing, or cooperating with another hardware device orcomponent (e.g., a co-processor and the like) to perform the operations.

The processor executing the computer readable or software instructionsrelating to the above described method(s) can be perceived as aprogrammed processor or a specialized processor. As such, the presentmodule 405 for learning and facilitating interaction routines (includingassociated data structures) of the present disclosure can be stored on atangible or physical (broadly non-transitory) computer-readable storagedevice or medium, e.g., volatile memory, non-volatile memory, ROMmemory, RAM memory, magnetic or optical drive, device or diskette andthe like. More specifically, the computer-readable storage device maycomprise any physical devices that provide the ability to storeinformation such as data and/or instructions to be accessed by aprocessor or a computing device such as a computer or an applicationserver.

While various examples have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. Thus, the breadth and scope of a preferred example shouldnot be limited by any of the above-described example examples, butshould be defined only in accordance with the following claims and theirequivalents.

What is claimed is:
 1. A method, comprising: learning an interactionroutine conducted between a human user and a second party, wherein theinteraction routine comprises a series of prompts and responses designedto identify and deliver desired information; storing a template of theinteraction routine based on the learning, wherein the template includesat least a portion of the series of prompts and responses; detecting, inthe course of a new instance of the interaction routine, at least oneprompt from the second party that requests a response from the humanuser; and using the template to provide a response to the at least oneprompt from the second party so that involvement of the human user inthe new instance of the interaction routine is minimized.
 2. The methodof claim 1, wherein the second party is an automated system.
 3. Themethod of claim 1, wherein the second party is another human.
 4. Themethod of claim 1, wherein the second party includes both an automatedsystem and another human.
 5. The method of claim 1, wherein the storingthe template comprises: recording a first prompt of the series ofprompts and responses; and recording a first response of the series ofprompts and responses, wherein the first response is responsive to thefirst prompt.
 6. The method of claim 5, wherein the using the templatecomprises: detecting that the at least one prompt from the second partymatches the first prompt; and responding to the at least one prompt fromthe second party with the first response.
 7. The method of claim 6,wherein the first prompt is a prompt to select an option from a menu. 8.The method of claim 7, wherein the first response indicates a pushing ofa button corresponding to an option from the menu.
 9. The method ofclaim 7, wherein the first response comprises a recording of the humanuser speaking an option from the menu.
 10. The method of claim 6,wherein the first prompt is a request to state a reason for aninitiation of the interaction routine.
 11. The method of claim 10,wherein the first response comprises a recording of the human userspeaking the reason.
 12. The method of claim 6, wherein the first promptis a request to speak a voice passphrase.
 13. The method of claim 12,wherein the first response comprises a recording of the human userspeaking the voice passphrase.
 14. The method of claim 12, wherein thefirst response comprises prompting the human user to speak the voicepassphrase.
 15. The method of claim 5, wherein the using the templatecomprises: detecting that the at least one prompt from the second partydoes not match any prompts in the template; and invoking an alternatemeans of providing the response to the at least one prompt from thesecond party.
 16. The method of claim 15, wherein the invoking thealternate means comprises: requesting intervention from the human user.17. The method of claim 15, wherein the invoking the alternate meanscomprises: providing a pre-defined default response in response to theat least one prompt from the second party.
 18. The method of claim 15,wherein the invoking the alternate means comprises: adapting the firstresponse so that it is responsive to the at least one prompt from thesecond party.
 19. A device, comprising: a processor; and acomputer-readable medium storing instructions which, when executed bythe processor, cause the processor to perform operations comprising:learning an interaction routine conducted between a human user and asecond party, wherein the interaction routine comprises a series ofprompts and responses designed to identify and deliver desiredinformation; storing a template of the interaction routine based on thelearning, wherein the template includes at least a portion of the seriesof prompts and responses; detecting, in the course of a new instance ofthe interaction routine, at least one prompt from the second party thatrequests a response from the human user; and using the template toprovide a response to the prompt so that involvement of the human userin the new instance of the interaction routine is minimized.
 20. Acomputer-readable medium storing instructions which, when executed bythe processor, cause the processor to perform operations comprising:learning an interaction routine conducted between a human user and asecond party, wherein the interaction routine comprises a series ofprompts and responses designed to identify and deliver desiredinformation; storing a template of the interaction routine based on thelearning, wherein the template includes at least a portion of the seriesof prompts and responses; detecting, in the course of a new instance ofthe interaction routine, at least one prompt from the second party thatrequests a response from the human user; and using the template toprovide a response to the prompt so that involvement of the human userin the new instance of the interaction routine is minimized.